README.md

LLMs in MLX with GGUF

An example generating text using GGUF format models in MLX.¹

Note

MLX is able to read most quantization formats from GGUF directly. However, only a few quantizations are supported directly: Q4_0, Q4_1, and Q8_0. Unsupported quantizations will be cast to float16.

Setup

Install the dependencies:

pip install -r requirements.txt

Run

Run with:

python generate.py \
  --repo <hugging_face_repo> \
  --gguf <file.gguf> \
  --prompt "Write a quicksort in Python"

For example, to generate text with Mistral 7B use:

python generate.py \
  --repo TheBloke/Mistral-7B-v0.1-GGUF \
  --gguf mistral-7b-v0.1.Q8_0.gguf \
  --prompt "Write a quicksort in Python"

Run python generate.py --help for more options.

Models that have been tested and work include:

• TheBloke/Mistral-7B-v0.1-GGUF, for quantized models use:

  • mistral-7b-v0.1.Q8_0.gguf
  • mistral-7b-v0.1.Q4_0.gguf

• TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF, for quantized models use:

  • tinyllama-1.1b-chat-v1.0.Q8_0.gguf
  • tinyllama-1.1b-chat-v1.0.Q4_0.gguf

• Jaward/phi-3-mini-4k-instruct.Q4_0.gguf, for 4 bits quantized phi-3-mini-4k-instruct use:

  • phi-3-mini-4k-instruct.Q4_0.gguf

Footnotes

1. For more information on GGUF see the documentation. ↩